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Sports Analytics Machine Learning Jobs in Cary, NC

IQVIA

AI Solutions Delivery 3 (Remote)

Durham, NC · Remote

Our team offerings leverage advanced analytics, machine learning algorithms, and technology platforms for a variety of healthcare applications including finding undiagnosed patients with rare ...

IQVIA

AI Solutions Delivery 3 (Remote)

Durham, NC · Remote

Our team offerings leverage advanced analytics, machine learning algorithms, and technology platforms for a variety of healthcare applications including finding undiagnosed patients with rare ...

LexisNexis

Director, Data Sciences

Raleigh, NC · On-site

Oversee advanced data analysis, modeling, and machine learning to develop predictive and prescriptive models that drive business outcomes. • Data Collection and Preparation: Establish protocols for ...

Deloitte

Lead AI and Data Science Engineer II

Raleigh, NC · On-site

$99K - $131K/yr

In this role, you will lead complex data science work that combines research design, statistical analysis, machine learning, and application development to solve high-priority people challenges. You ...

LexisNexis

Director, Data Sciences

Raleigh, NC · On-site +1

Data Analysis and Modeling : Oversee advanced data analysis, modeling, and machine learning to develop predictive and prescriptive models that drive business outcomes. * Data Collection and ...

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Sports Analytics Machine Learning information

What is sports analytics machine learning?

Sports analytics machine learning is the application of data science and machine learning techniques to analyze sports data, such as player statistics, game outcomes, and biometric information. Professionals in this field develop models to identify patterns, predict player performance, optimize team strategies, and gain competitive advantages. This work involves collecting large datasets, cleaning and processing data, and using algorithms to extract actionable insights that can benefit teams, coaches, and athletes. Sports analytics with machine learning is increasingly used in professional sports to inform decisions about training, recruitment, and game tactics.

How do sports analytics machine learning professionals typically collaborate with coaches and athletes to impact game strategy?

Sports Analytics Machine Learning professionals often work closely with coaches and athletes by translating complex data insights into practical recommendations. They attend strategy meetings, present findings through visualizations, and help interpret trends that can influence training, player selection, and in-game tactics. Effective communication is key, as these professionals must bridge the gap between technical analyses and real-world sports applications. This collaborative environment not only enhances team performance but also provides opportunities to see the direct impact of your work on the field.

What are the key skills and qualifications needed to thrive as a sports analytics machine learning specialist, and why are they important?

To thrive as a Sports Analytics Machine Learning Specialist, you need a strong background in statistics, data analysis, programming (typically in Python or R), and an understanding of machine learning algorithms, often supported by a degree in data science, statistics, or a related field. Familiarity with data visualization tools, sports databases, and machine learning frameworks like TensorFlow or scikit-learn is essential, along with experience using SQL and data pipelines. Strong problem-solving, communication, and collaboration skills help translate complex data findings into actionable insights for coaches, players, and stakeholders. These skills are crucial for extracting meaningful patterns from vast sports datasets and driving performance improvements or strategic decisions within sports organizations.

What are popular job titles related to Sports Analytics Machine Learning jobs in Cary, NC?

For Sports Analytics Machine Learning jobs in Cary, NC, the most frequently searched job titles are:

What job categories do people searching Sports Analytics Machine Learning jobs in Cary, NC look for?

The top searched job categories for Sports Analytics Machine Learning jobs in Cary, NC are:

What cities near Cary, NC are hiring for Sports Analytics Machine Learning jobs?

Cities near Cary, NC with the most Sports Analytics Machine Learning job openings:

Sports Analytics Machine Learning jobs near you

Principal Machine Learning Engineer I

LexisNexis

Raleigh, NC

$136K - $252K/yr

Full-time

Re-posted 3 days ago

LexisNexis rating

7.6

Company rating: 7.6 out of 10

Based on 17 frontline employees who took The Breakroom Quiz

190th of 491 rated business services

Job description

hackajob is collaborating with LexisNexis to connect them with exceptional professionals for this role.

About our Team

LexisNexis Legal & Professional, which serves customers in more than 150 countries with 11,800 employees worldwide, is part of RELX (www.relx.com), a global provider of information-based analytics and decision tools for professional and business customers. Our company has been a long-time leader in deploying AI and advanced technologies to the legal market to improve productivity and transform the overall business and practice of law, deploying ethical and powerful generative AI solutions with a flexible, multi-model approach that prioritizes using the best model from today’s top model creators for each individual legal use case. The company employs over 2,000 technologists, data scientists, and experts to develop, test, and validate solutions in line with RELX Responsible AI Principles (https://stories.relx.com/responsible-ai-principles/index.html).

About the Role

Do you love collaborating with teams to solve complex technical problems?

We are seeking a Principal Machine Learning Engineer to design, build, and operate scalable AI/ML systems and agentic architectures that support next-generation legal research and analytics products. This role combines deep ML expertise with distributed systems engineering and AI platform development.

You will play a key role in developing enterprise-grade AI systems, including large language model (LLM) infrastructure, retrieval-augmented generation (RAG) pipelines, and autonomous agent frameworks designed for complex large unstructured data.

Responsibilities:

  • Provide architectural direction and code-level guidance.

  • Establish engineering best practices for ML system design, testing, and deployment.

  • Conduct design reviews, performance reviews, and technical roadmap planning.

  • Architect distributed ML systems serving multiple global products.

  • Standardize infrastructure patterns for LLM serving and retrieval systems.

  • Define and implement enterprise-ready agentic frameworks.

  • Architect multi-step reasoning systems.

  • Lead decisions on deterministic workflows vs. autonomous agents.

  • Implement guardrails, safety layers, and traceability mechanisms.

  • Develop evaluation frameworks to measure reasoning quality, hallucination rates, and reliability.

  • Establish CI/CD standards for ML lifecycle management.

  • Ensure compliance with enterprise data governance and responsible AI standards.

Requirements

  • 10 + years of Machine Learning/Software Engineer experience

  • Master’s degree or bachelor's degree, computer science degree is highly desirable.

  • Strong software engineering background with experience in building system design, architecting AI feature/products that caters large number of users and deals with large volume of unstructured data

  • Experience with ML deployment to production

U.S. National Base Pay Range: $136,100 - $252,800. Geographic differentials may apply in some locations to better reflect local market rates.

This job is eligible for an annual incentive bonus.

We know your well-being and happiness are key to a long and successful career. We are delighted to offer country specific benefits. Click here to access benefits specific to your location.

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We are an equal opportunity employer: qualified applicants are considered for and treated during employment without regard to race, color, creed, religion, sex, national origin, citizenship status, disability status, protected veteran status, age, marital status, sexual orientation, gender identity, genetic information, or any other characteristic protected by law.

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